Cappable-Seq and Direct RNA Sequencing Reveals Novel insights into the Transcriptome of Listeria monocytogenes

doi:10.21203/rs.3.rs-3996292/v1

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Cappable-Seq and Direct RNA Sequencing Reveals Novel insights into the Transcriptome of Listeria monocytogenes

https://doi.org/10.21203/rs.3.rs-3996292/v1

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Background

Listeria monocytogenes is a foodborne pathogen that can survive various stresses. To inactivate Listeria monocytogenes, food processing facilities use high energy methods, such as high-pressure processing (HPP). In this study, we explored the transcriptional units of barotolerant L. monocytogenes RO15 using Cappable-seq and direct RNA sequencing, two novel techniques.

Results

We detected 1641 transcription start sites (TSSs) in L. monocytogenes RO15, including six HPP-specific TSSs, showing that HPP influences the TSS selection. In addition, we predicted small RNAs (sRNAs) candidates and examined promoter motifs, which revealed new regulatory elements that control gene expression. By integrating short and long RNA-seq reads, we predicted the operon structure of L. monocytogenes RO15 and found 658 operons, comprising 71% of all the genes. The largest operons were mainly located in prophage regions. Moreover, we identified A-to-I RNA editing events in L. monocytogenes for the first time. HPP treatment statistically significantly (p < 0.05) increased the A-to-I editing of several genes including hpf and mdxE suggesting a role in the stress response. We predicted m6A RNA modifications in L. monocytogenes RO15 using direct RNA sequencing reads. This is the first report of m6A RNA modifications in L. monocytogenes by using direct RNA sequencing.

Conclusions

This study provides novel insights into the transcriptome complexity and diversity, stress response strategies, and post-transcriptional modifications of L. monocytogenes. Our results uncover the genomic mechanisms of adaptation of L. monocytogenes to HPP and indicate potential targets for developing new strategies to control this pathogen. However, further studies are needed to validate the functional roles of the identified sRNAs, RNA editing events, and RNA modifications in L. monocytogenes.

RNA editing

transcription start site

high pressure processing

pathogen

direct RNA sequencing

long sequencing reads

operon

Pathogens in food cause hardship for societies by increasing waste, carbon footprint, and economic losses [1]. Listeria monocytogenes, a major contributor to these losses [2], causes listeriosis [3], a deadly infection that affects people with weak immunity [4]. Listeriosis has been linked to outbreaks caused by contaminated milk, fish, cheese, ready-to-eat foods, vegetables, and meat [5, 6]. L. monocytogenes survives in various stress conditions, such as low temperatures, high pressures, and acidity, challenging the food industry [3, 7]. Thus, food products should be treated carefully to prevent L. monocytogenes contamination or survival.

High pressure processing (HPP) is a non-thermal method to inactivate pathogenic microbes, including Listeria sp. [8]. Previously, we have studied the effect of HPP on L. monocytogenes, in particular its recovery after HPP stress [9–13]. We have previously shown that genes related to ribosome hibernation, protein folding, the phosphotransferase system (PTS), prophages, and cobalamin biosynthesis play a role in HPP stress and recovery in L. monocytogenes [13]. In addition to recent efforts, here we wanted to deepen the L. monocytogenes genomic knowledge with a new perspective around whole transcriptome units, transcription start site (TSS), gene structure, RNA modification, and RNA editing.

In bacteria, RNA polymerase (RNAP) initiates transcription by recognizing and binding to specific sequence elements on the DNA. The first DNA nucleotide position that is transcribed into RNA by the RNAP complex is defined as the TSS. Identification of TSS can be done with specific RNA-seq protocols (differential RNA-seq and Cappable-seq), both of which enrich the 5′ end of transcripts [14, 15]. Previous studies have shown that identification of TSS is useful for investigating RNAP binding sites, finding novel genes and small RNAs (sRNAs), and identifying global transcriptional units in several bacterial species [16–20]. In L. monocytogenes specifically, TSS identification was previously done on the reference strain (EGD-e) [19]. Here, we have used a different strain (RO15) and a different method to expand TSS identification in L. monocytogenes; moreover, we performed a comparative analysis to test if there is a TSS difference between HPP-treated and control samples.

Adenosine-to-inosine (A-to-I) RNA editing is a post-transcriptional modification that occurs in both eukaryotes and bacteria [21]. In bacteria, A-to-I editing is catalyzed by the TadA enzyme. TadA deaminates adenosine (A) to inosine (I) [22, 23]. A-to-I RNA editing is thought to play a role in a variety of biological processes in bacteria. For example, in Escherichia coli, A-to-I editing regulates toxicity and toxin activity [22]. In Pseudomonas putida, A-to-I editing regulates stress adaptation and pathogenicity [23]. Moreover, the GACG motif has been shown to be the binding target for the tadA gene in mRNAs [23]. A-to-I RNA editing, to our knowledge, has not been demonstrated in L. monocytogenes thus far. Thus, in this study we re-analyzed our previous public Illumina RNA-seq data [13] with the hypothesis of whether A-to-I RNA editing plays a role in the HPP injury stress adaptation and recovery.

RNA molecules have been studied by short read sequencing methods like Illumina in the past [13, 24, 25]. However, direct RNA sequencing of long continuous RNA molecules is possible with Oxford Nanopore Technology (ONT) [26]. ONT direct RNA sequencing is a PCR-free method that directly sequences native RNA molecules. Therefore, the method can identify RNA modifications directly from the assayed molecules [26]. ONT direct RNA-seq has been shown to correctly detect rRNA base modifications in E. coli [27]. In this study, we used direct RNA-seq for the first time on two different L. monocytogenes strains.

We have been working on the L. monocytogenes response to HPP, aiming to improve inactivation of these bacteria. Our studies were performed at the level of comparing genomes and gene expression using DNA sequencing and RNA-seq approaches combined with bioinformatics [11–13]. In general, there are only limited datasets available on organisation of the transcriptional units of L. monocytogenes [19]. To address the gaps in our understanding of L. monocytogenes genes, transcriptional unit structure, and regulation, we performed two novel sequencing methods: Cappable-seq and direct RNA-seq. These methods helped us to predict transcription start sites (TSSs), operon structures, promoter motifs, and N6-methyladenosine (m6A) RNA modification, as well as the effect of HPP on transcriptional units. Moreover, we re-analyzed our existing Illumina RNA-seq data [13] from a RNA-editing perspective, which showed potential usage of A-to-I RNA editing in HPP response in L. monocytogenes. These findings can open up new avenues for research and innovation in food safety and genomics.

Experimental setup, sampling, RNA extraction

For this study we used the same RNA extracts that we have obtained in our previous study [13]. Briefly, in our previous study [13], we used two L. monocytogenes strains: strain RO15 (Genbank: GCA_902827145.1) and strain ScottA (GenBank: CM001159.1). Both strains were cultivated in BHI broth (350 ml) at 37°C for 24 hours, to the early stationary phase. Further, the cell cultures were transferred to 2 mL tubes and cooled at 4°C for 1 h. The samples were treated at 200 MPa and 400 MPa, 8°C, for 8 min in multi-vessel high-pressure equipment. The samples were kept at 8°C for recovery at atmospheric pressure. They were sampled at 0, 5, 10, 30, 45, and 60 minutes, and at 6, 24, and 48 hours (T1 to T9) after HPP. Control samples were also stored at 8°C with no pressure and sampled at the same time points as HPP treated samples. RNA was extracted from samples using the NucleoSpin RNA kit [13].

Previously, 216 samples were subjected to RNA-seq using Illumina [13]. In this study, we chose six strain RO15 samples from previously obtained RNA extracts to perform additional Cappable-seq. The selected strain RO15 samples for Cappable-seq were: R017 (200 MPa HPP, 0 min time point), R101 (400 MPa HPP, 0 min time point), R173 (400 MPa HPP, 24 hours time point), R179 (control, 0 min time point), R250 (control 0 min time point), R312 (control 24 hours time point).

For the direct RNA-seq, we chose two strain RO15 samples, and two strain ScottA samples. Selected strain RO15 samples for direct RNA-seq were: R173 (400 MPa HPP, 24 hours time point) and R312 (control 24 hours time point). Selected strain ScottA samples were: R158 (400 MPa HPP, 24 hours time point) and R317 (control 24 hours time point) (Table S1).

Cappable-seq library processing and sequencing

To capture of the 5′ end of primary transcripts we used the Cappable-seq method [15] and the protocol published by New England Biolabs (https://international.neb.com/protocols/2018/01/19/cappable-seq-for-prokaryotic-transcription-start-site-determination). Before Cappable-seq library preparation, 15 µl of decapped RNA was vacuum-dried in DNA Speed Vac DNA110 (Savant) at a low drying rate for about 40 min and then suspended in 2.5 µl of ultra-pure water. Cappable-seq libraries were prepared with TruSeq Small RNA Library Prep (Illumina) using half of the kit’s volumes according to the manufacturer’s instructions. Libraries (half of the reaction volume) were pooled and concentrated using Amicon Ultra-0.5 100K filter device (Millipore) (Table S2). Fragments of a size of 140–250 bases fusing BluePippin and 3% agarose gel cassette (Sage Science). NextSeq 500 (Illumina) was used to sequence the Cappable-seq libraries with single-end 75 bases long reads.

Read processing and mapping for TSS

The same preprocessing was performed for Cappable-seq reads as described previously for Illumina RNA-seq reads [13], except using “TGGAATTCTCGGGTGCCAAGG” as an adapter during the adapter filtering step. Both Illumina RNA-seq and Cappable-seq reads were mapped to the strain RO15 genome (Genbank: GCA_902827145.1) using the READemption v0.5.0 pipeline [28] with default options. Briefly, the pipeline uses Segemehl v0.2 [29] with minimal accuracy of 95%. Coverage and normalization of coverage was calculated using READemption v0.5.0 “coverage” function. The coverage was normalized by the total number of aligned reads and multiplied by the lowest number of aligned reads of all input libraries.

Identification of TSSs in RO15

To get the whole TSS set, we combined all six Cappable-seq reads files into one, and the same was done for corresponding six Illumina RNA-seq read files as well. TSS identification was done by comparing the relative coverage of reads between Cappable-seq reads and Illumina RNA-seq reads. For this purpose, we used ANNOgesic v1.0.16 pipeline [30] with TSSpredator v1.06 [31]. To get the optimal parameters, we first manually annotated the first 50 kbp region. Manually annotated file was employed to obtain the optimal parameters using ‘annogesic optimize_tss_ps’. Then, “annogesic tss_ps” command was run using ‘-c normPercentile = 0.8, texNormPercentile = 0.2, allowedCompareShift = 4, allowedRepCompareShift = 4’.

We predicted HPP-specific transcription start sites (TSSs) by comparing TSS region Cappable-seq read counts from HPP-treated and control samples. We used featureCounts v2.0.3 [32] to obtain TSS region Cappable-seq read counts from all six sequenced samples. Since we had three HPP-treated samples and three control samples, we ran DESeq2 to compare TSS region read counts between the two conditions. We normalized the counts and defined a TSS as HPP-specific if it met the following two criterias: The log2 fold change between the HPP-treated and control samples was greater than or equal to |4|. The total read count for the opposite condition was less than 10 (in total for triplicates).

Identification of UTR and sRNA in RO15

We used ANNOgesic v1.0.16 pipeline [30] to predict additional sRNA and UTR “annogesic utr” with default options was used for UTR length prediction. “annogesic srna” with ‘--tex_notex 1’ were used for sRNA prediction.

EGD-e TSS lift over to RO15

The genome sequences of EGD-e and RO15 were aligned using LASTZ v1.04.15 [33] with “--chain --format = axt” options. We then chained the “axt” alignment files using axtChain [34] and generated chain format output. Chain format output was used to lift EGD-e TSS gff to RO15 genome using CrossMap v0.5.4 [35].

Variant calling

We used the Illumina RNA-seq data obtained in our HPP experiment study [13]. Illumina RNA-seq preprocessing was described previously [13]. Illumina RNA-seq reads were mapped to the reference genome using Bowtie2 v2.3.4.3 [36] default options. The output was sorted and converted to BAM format using Samtools v1.9 [37]. Bcftools v1.9 [38] “mpileup” function with default options was used to generate genotype likelihoods. Bcftools v1.9 “call” function with “-mv -Ob” options was used for variant calling.

Direct RNA-seq

Four RNA samples were additionally analysed with direct RNA-seq. Since the protocol assumes mRNAs to have poly-A tails, they were first added by treating 14.5 µl of total RNA (3-7.8 µg) with 7.5 units of E. coli poly(A) polymerase (New England Biolabs) and 2 mM ATP in 1x reaction buffer at 37°C for 30 min. To enrich the yield of mRNA reads, the reaction included a mix of nine rRNA blocking oligos (total concentration: 2.4 µM) (Table S2). Blocking of the 3’ ends of 16S, 23S, and 5S rRNA leads to preferential addition of poly-A tails to mRNA molecules [39]. The reactions were purified with 1.8 vol RNA Clean XP beads and eluted in RNase free water. The rRNA-depleted RNAs with poly-A tails were then used as starting material for the Direct RNA-seq protocol SQK-RNA002 (Oxford Nanopore Technologies) according to the manufacturer’s instructions, except for RNA Control Strand (RCS), which was diluted 1:20 prior to use (Fig. 1). About 190 ng of the reverse-transcribed and adapted original RNA molecules were sequenced with FLO-MIN106 flow cells using MinKNOW software v19.06.8 or 21.06.0 (Oxford Nanopore Technologies). The number of reads obtained for the four samples ranged from 314,044 to 1,613,144. The percentage of reads that mapped the reference genome was between 43.61% and 93.04% of the total reads. The aligned reads consisted of 10.25–25.31% mRNA reads.

m6A RNA modification prediction using direct RNA-seq

To identify m6A RNA modifications, we used ModPhred v3.6.1 [40] in conjunction with an m6A modification-aware basecalling model [41]. Each of the four samples (two RO15 and two ScottA samples) was individually assessed using their direct RNA-seq reads to determine the locations of m6A RNA modifications. The prediction was made using the default threshold, which requires at least 25 read coverage and at least 5% modification frequency.

Annotation of operons

Operon structure was predicted using COSMO v0.1.0 [42] with default options. COSMO was run with all RNA-seq reads (both Illumina RNA-seq and direct RNA-seq).

TSS identification in L. monocytogenes strain RO15

Based on the comparison of the relative coverage of reads between Cappable-seq and Illumina RNA-seq with ANNOgesic tool, 1641 TSSs were identified across the genome of L. monocytogenes RO15 (Table S3). Among all TSS, 1233 had highest coverage within 300 bp upstream of an open reading frame (ORF) and were classified as primary TSS. Secondary TSSs (i.e. TSSs with lower coverage within 300 bp upstream of an ORF) were detected for 90 genes. The number of TSSs located within the coding sequence of a gene (i.e. internal TSSs) was 233. Moreover, 199 TSSs located on the antisense strand of a protein coding gene (antisense TSSs) and 55 orphan TSSs not assigned to any coding DNA sequence (CDS) were detected. To facilitate visual inspection of these results, we created a public online genome viewer page (https://icemduru.github.io/listeria_ro15_transcript/).

The TSSs of L. monocytogenes strain EGD-e have been previously identified, and strain EGD-e was shown to contain 1576 TSSs [19]. To compare our TSS prediction in RO15 and EGD-e TSSs, [19] we mapped the EGD-e TSSs to the RO15 genome. Almost all EGD-e TSSs (1531 of 1576) were successfully mapped to the RO15 genome and the following analysis revealed that 876 of 1531 lifted TSS positions were the same as the TSS positions that we predicted in RO15 (Table S3).

By comparing the Cappable-seq reads of the HPP-treated samples to those of the control samples, we were able to predict TSSs that were specific to the HPP treatment. In total six TSS were predicted to be specific to HPP-treated samples. The six TSSs specific to HPP-treated samples were associated with five genes and one tRNA (Table 1).

Table 1

**Predicted transcription start sites (TSS) specific to HPP-treated samples.** The first column shows the TSS identifier (TSS ID), which consists of the genomic position and the strand orientation (forward or reverse) of the TSS. The second column shows the gene identifier (gene ID) of the gene that is transcribed from the TSS. The third column shows the gene annotation, which describes the putative function of the gene product. The fourth column shows the TSS type, which indicates whether the TSS is located at the 5’ end of a gene (primary), within a gene (internal), or opposite to a gene (antisense). The fifth column shows the average number of Cappable-seq reads for all three HPP treated samples. The sixth column shows the average number of Cappable-seq reads for all three control samples. The seventh column shows the log₂ fold change of the average Cappable-seq reads between the HPP and control samples.
TSS ID	Associated gene	Associated gene annotation	TSS type	Avg. Cappable-seq reads (HPP)	Avg. Cappable-seq reads (Control)	Log₂ fold change
TSS:2221338_f	OCPFDLNE_02235	Phage terminase subunit	antisense	11	0	8.43
TSS:398878_f	OCPFDLNE_00383	Ferrous iron permease EfeU	primary	15.3	2	7.24
TSS:2273494_r	OCPFDLNE_02283	Probable transcriptional regulator YvdE	internal	16	0	4.80
TSS:76604_f	OCPFDLNE_00071	hypothetical protein	internal	19.6	4	4.34
TSS:630711_r	OCPFDLNE_00601	Homoserine O-acetyltransferase	internal	12.3	0	6.32
TSS:246761_f	OCPFDLNE_00242	tRNA-Arg	primary	11	0	6.50

TSS of prophages

Our previous studies investigating the genome of RO15 strain [12] and gene expression in response to HPP stress [13] revealed that prophages were associated with the phenotypic characteristics of the strain. Notably, we observed upregulation of phage genes during HPP stress, suggesting induction of prophages during HPP stress [13]. This led us to further explore phage TSSs, which previously have received limited attention in L. monocytogenes strains.

Previously we identified five distinct prophage regions in the RO15 genome, ranging in size from 10.7 kb (prophage 1) to 41.3 kb (prophage 5) [12]. Prophage 5 was further observed as a circular form, suggesting a free virus genome version [12]. Among these regions, prophage 5 harboured the highest number of TSSs, with a significantly higher proportion of antisense TSSs compared to the other prophages (Table 2).

Table 2

**Prophage regions in RO15 and the number of TSS**.
Prophage Region	Number of genes	Primary TSS	Internal TSS	Antisense TSS	Orphan TSS	Total
Prophage 1 (125824–136550 bp)	17	1	1	0	0	2
Prophage 2 (673631–706883 bp)	44	1	4	1	0	6
Prophage 3 (2175980–2223958 bp)	79	7	4	3	1	15
Prophage 4 (2559206–2601984 bp)	64	7	0	3	2	12
Prophage 5 (2729417–2770759 bp)	67	10	1	7	0	18

Interestingly, one of the HPP-specific TSS (TSS:2221338_f) was located in the prophage 3 region. This TSS, which is antisense to the gene OCPFDLNE_02235 (encoding phage terminase large subunit protein), was specifically induced in response to HPP stress. Cappable-seq analysis revealed the presence of TSS signals at this location in all HPP-treated samples, while no such signal was detected in control samples. Additionally, short Illumina RNA-seq reads indicated the presence of opposite-strand reads in the OCPFDLNE_02235 gene region, supporting the potential involvement of antisense RNA in this gene. However, no sRNA was predicted on this region in our sRNA prediction workflow

Prediction of sRNAs in L. monocytogenes strain RO15

The ANNOgesic tool was used to predict sRNAs in L. monocytogenes RO15, resulting in 81 identified candidates (Table S4). The majority (53 of 81) were classified as intergenic sRNAs. A total of 20 sRNAs were found to originate from untranslated regions (UTRs), with four derived from 3'UTRs and 16 from 5'UTRs. Six of the predicted sRNAs were identified as antisense sRNAs (asRNAs), and two were classified as InterCDS sRNAs (located within coding sequences).

To investigate asRNAs and gene expression patterns during high-pressure processing (HPP) treatment, gene counts were performed. However, no anticorrelation was observed between fold changes in asRNAs and their corresponding genes (Figure S1).

In a previous study, nine antisense RNAs (asRNAs) were experimentally validated (by Northern blot) in Listeria monocytogenes strain EGD-e [19]. To investigate these asRNAs in strain RO15, we mapped them to the genome of strain RO15. We then examined our TSS prediction data for RO15 and found that two of these nine validated asRNAs had corresponding TSSs in RO15. Interestingly, only one of our predicted sRNA was in the same genomic location as a validated asRNAs in EGD-e. However, our prediction classified this sRNA as intergenic in RO15 because the adjacent gene was not found in RO15.

Prediction of promoters, and UTR length in L. monocytogenes strain RO15

We analyzed the upstream regions of all predicted TSSs (-50 bp to 1 bp) using the MEME suite to identify promoter motifs in L. monocytogenes. Our findings revealed that there is only one well-conserved region in the promoter regions, with a -10 position “TATAAT”-like motif. This motif is present in all three types of TSSs. Additionally, we observed that the − 10 position motif is slightly different in secondary TSS promoter regions compared with other types of TSS regions. This suggests that there may be some subtle differences in the regulation of secondary TSSs compared with other types of TSSs (Fig. 2).

We investigated the length distribution of 5' UTRs in L. monocytogenes RO15 using TSSs and calculating UTR lengths. Our analysis revealed that most of the 5' UTRs were shorter than 100 bases (Figure S2). We observed that the median length of 5' UTRs using primary TSSs was 33 bases. When secondary TSSs were also considered, the median UTR length increased to 34 bases (Figure S2).

Operon prediction in L. monocytogenes strain RO15

By using a combination of Illumina short and ONT direct long RNA-seq reads, we analyzed the operon structure of the L. monocytogenes RO15 genome. Our predictions revealed that 71% (2210 genes) of the genes were organized into operons (Table S5). A total of 658 operons were identified, with an average of 3.35 genes per operon (median = 2 genes). The largest predicted operons were predominantly found within the prophage regions. Large operons were also seen in the L. monocytogenes RO15 genome, such as the cobalamin biosynthesis operon, comprising 19 genes (OCPFDLNE_01235 - OCPFDLNE_01253), emerged as one of the largest operons within the entire genome.

Furthermore, we visually examined our predictions and observed strong support from both Cappable-seq and direct RNA sequencing reads. For instance, the manXYZ operon (OCPFDLNE_00834 - OCPFDLNE_00837) was predicted by our workflow, and both Cappable-seq and direct RNA sequencing reads helped us visually confirm the operon prediction (Fig. 3).

A-to-I RNA editing in L. monocytogenes strain RO15 and strain ScottA

During the TSS analysis, we carefully examined the previously published Illumina RNA-seq data from RO15 and ScottA strains [13]. Our analysis revealed several sequence variants that were not attributed to sequencing errors and were not detected in the gDNA-based PacBio or Illumina data [12]. These variants, consisting of A to G (T to C for reverse strand) transitions, are indicative of A-to-I RNA editing, a post-transcriptional modification that has been previously studied in other bacteria [22, 23].

The presence of A-to-I RNA editing variants was higher in HPP-treated samples for several genes (Table 3, Table 4, Table S6). A subset of these variants was specifically observed in HPP-treated samples, suggesting their potential role in HPP response mechanisms. A-to-I RNA editing within genes spoVG, pbpX, mdxE, patB, hpf, and pepC was particularly common in both strains under HPP stress. While the majority of observed A-to-I RNA editing variants were confined to HPP-treated samples, a subset of these variants were also detected in control samples. These variants, found within genes pflA, mdxE, secA, and kdpD in strain RO15, and actA, ldh, dhaS, LMOSA_26480, and mdxE in strain ScottA. Notably, most of these variants were observed as partial variants, indicating that approximately half of the RNA-seq reads contained the variant base in the transcript.

Table 3

**Summary of A > G (T > C on reverse strand) variant positions and the corresponding gene in strain RO15.** The table shows the positions of A > G variants in RNA of RO15, and the locus tag, gene name, number of samples with the variant in treated and control groups, reference and variant amino acids of the corresponding gene, respectively. The p-values between treated and control samples were calculated using Fisher’s exact test. For the sake of clarity, only variants detected in more than 7 samples are presented. The complete list of variants is available in the supplementary Table S6.
Variant Pos.	Locus Tag	Gene Name	# variants treated (n = 51)	# variants control (n = 52)	ref. aa	var. aa	p-value
2269171	OCPFDLNE_02280	mdxE	37	10	Y	C	4.90E-08
2649787	OCPFDLNE_02685	hpf	21	1	L	L	3.47E-07
497744	OCPFDLNE_00478	dhaS	12	0	T	A	9.18E-05
1768810	OCPFDLNE_01761	yfhP	10	0	L	L	4.90E-04
1544500	OCPFDLNE_01549		9	0	Y	C	1.11E-03
429958	OCPFDLNE_00419	mngB	8	0	Y	C	2.47E-03
204136	OCPFDLNE_00200	spoVG	9	1	Y	C	7.41E-03
2428075	OCPFDLNE_02434	pepC	8	1	M	V	1.50E-02
2288303	sRNA_92	rli47	8	1	-	-	1.50E-02
2646986	OCPFDLNE_02684	secA	26	14	L	L	1.52E-02
1451307	OCPFDLNE_01455	pflA	51	47	L	L	2.70E-02
1213571	OCPFDLNE_01217	pdtaS	9	3	L	L	6.98E-02

Table 4

**Summary of A > G (T > C on reverse strand) variant positions and the corresponding gene in strain ScottA.** The table shows the positions of A > G variants in RNA of RO15, and the locus tag, gene name, number of samples with the variant in treated and control groups, reference and variant amino acids of the corresponding gene, respectively. The p-values between treated and control samples were calculated using Fisher’s exact test. For the sake of clarity, only variants detected in more than 7 samples are presented. The complete list of variants is available in the supplementary Table S6.
Variant Pos.	Locus Tag	Gene Name	# variants treated (n = 53)	# variants control (n = 54)	ref. aa	var. aa	p-value
2675586	LMOSA_4770	hpf	21	0	L	L	3.21E-08
1717344	LMOSA_25410	pepV	15	1	Y	C	8.61E-05
242871	LMOSA_10890	spoVG	12	0	Y	C	1.08E-04
2324765	LMOSA_1570	gloA	12	0	Y	C	1.08E-04
720351	intergenic_region		10	0	-	-	5.55E-04
1775078	LMOSA_25880	-	10	0	T	A	5.55E-04
2520887	LMOSA_3290	patB	10	0	T	A	5.55E-04
2481510	LMOSA_2970	pepC	11	1	M	V	1.91E-03
s252859	LMOSA_10970	actA	39	26	T	T	9.89E-03
2281668	LMOSA_1140	mdxE	14	4	Y	C	1.01E-02
2715706	LMOSA_5200	-	9	2	T	A	2.85E-02
1282535	LMOSA_21080	-	11	4	F	L	5.54E-02
568103	LMOSA_13830	dhaS	13	21	T	A	1.46E-01
1843087	LMOSA_26480	-	53	52	G	G	4.95E-01
257308	LMOSA_11030	ldh	53	54	I	V	1.00E + 00

Interestingly, we observed a gradual increase in the editing percentage with increasing HPP treatment time (Figure S3, Figure S4). However, the expression patterns of the genes harbouring these A-to-I RNA editing variants were not consistently altered. Both upregulation and downregulation were observed for these genes (Figure S5).

We examined the distribution of the RNA edited sites (Fig. 4). Our findings revealed distinct preferences for modified sites between the RO15 and Scott A strains. Notably, while the predicted consensus motif differed between the two strains, two four-base motifs (TACG and GTAA) emerged as the most prevalent edited motifs in both strains (Fig. 4).

RNA m6A modification prediction in L. monocytogenes strain RO15 and strain ScottA

In the RO15 HPP-treated sample, we predicted 23 sites with m6A modifications, while only three positions were identified as m6A modified in the RO15 control sample. m6A modifications targeting the dnaD and arsC genes were observed in both treated and control samples (Table S7). Notably, carbohydrate transmembrane transporter activity-related genes such as manX (encoding a mannose transporter), mntB (encoding a manganese transporter), and mdxE (encoding a Maltodextrin-binding protein) were frequently targeted by m6A modifications in the HPP-treated sample (Table S7).

In contrast to RO15, fewer m6A RNA modifications were observed in HPP treated samples of ScottA. In the treated sample, four positions were predicted as m6A modified, while in the control sample, 11 positions were predicted as m6A modified. The modifications that target the gadB gene were seen in both samples. In addition to the gadB gene, m6A modification was observed in the inlA gene (encoding an Internalin-A protein) (Table S7).

We used Cappable-seq, a novel method for directly enriching the 5' end of primary transcripts in prokaryotes [15], to comprehensively identify the TSSs of L. monocytogenes RO15. Our analysis revealed 1,641 TSSs in the strain RO15. To compare our TSS prediction in RO15 with the previously reported TSSs in strain EGD-e [19], we mapped the EGD-e TSSs to the RO15 genome using a lift-over (mapping) approach. We found that only 57% of the mapped EGD-e TSSs (876/1531) matched the TSSs that we predicted in RO15 using Cappable-seq. The remaining 43% of the lifted EGD-e TSSs (655/1531) either did not have a corresponding TSS in RO15 or had a different TSS position. This discrepancy could be attributed to several factors, such as the strain variation, the sequencing depth, the mapping algorithm, and the TSS prediction methods and criteria [43]. For instance, RO15 and EGD-e belong to different clonal complexes (CC155 and CC9, respectively) and multilocus sequence types (ST155 and ST35, respectively) [12], which could result in genomic rearrangements and mutations that affect the TSS locations. Previous studies on TSS conservation within Shewanella species reported a 63% conservation rate [44], aligning with our findings for the RO15 and EGD-e comparison.

We found that six TSSs in L. monocytogenes RO15 were activated by HPP. These TSSs corresponded to five genes and one tRNA, indicating that HPP might alter the TSS selection in L. monocytogenes. This could help us understand how L. monocytogenes responds to stress and how to prevent its growth and survival in food products. The genes associated with these TSSs are potential targets for future research. Previous studies have reported a large number of condition-specific TSSs in E. coli under different growth conditions [45]. However, we detected only six condition-specific TSSs in L. monocytogenes RO15, out of 1,641 total TSSs. This is partly because we used a different and more stringent method to identify TSSs based on differential Cappable-seq signals between HPP-treated and control samples. Our method required almost no Cappable-seq signal in all control samples to call a TSS as HPP-specific.

We previously showed that HPP stress induces phages in L. monocytogenes species and upregulates phage genes [12, 13]. To better understand the transcriptional regulation of phage genes, we analyzed the TSSs of phages in detail. We found that phage regions had fewer TSSs than the rest of the genome. The L. monocytogenes RO15 genome had 3,107 genes and 1,641 TSSs, resulting in a 53% gene/TSS ratio. In contrast, the average gene/TSS ratio for the five phages was 18%. Our operon prediction also indicated that the largest operons were mainly located in the prophage regions, suggesting that phage genes are co-regulated. This is consistent with previous reports of long transcriptional units in Herelleviridae bacteriophages [46]. Interestingly, we identified a HPP-specific TSS in the prophage 3 region, which was antisense to the OCPFDLNE_02235 gene encoding a phage terminase large subunit protein. This observation raises the possibility that alternative TSSs and antisense RNAs may play a role in regulating phage gene expression under stress conditions.

In Listeria, sRNAs play a role in several processes including pathogenicity and host interaction [47]. In this study, we predicted 81 sRNAs in L. monocytogenes RO15. We compared our sRNA predictions with those reported in L. monocytogenes EGD-e and other strains, and found that only two of the nine experimentally validated asRNAs in EGD-e had corresponding TSSs in RO15. This suggests that sRNA diversity and conservation may vary among different strains and species of L. monocytogenes. We also investigated the expression patterns of the predicted asRNAs and their target genes under HPP treatment, but did not observe any anticorrelation between them. This could be due to the complex and dynamic regulation of asRNAs in bacteria, which may depend on various factors such as transcription, translation, stability, and interaction of the RNA molecules [48]. The predicted sRNAs may play important roles in modulating gene expression and influencing the physiology and pathogenicity of L. monocytogenes RO15. However, the functions and mechanisms of the sRNAs remain largely unknown and require further experimental validation and characterization. Future studies should also explore how the sRNAs respond to different environmental and host signals, and how they interact with other regulatory elements in L. monocytogenes RO15.

The 5’ UTR of L. monocytogenes RO15 has a median length of 33 bases, which agrees with previous predictions for L. monocytogenes EGD-e and L. innocua BUG499 [19]. This implies that 5’ UTR length regulation may be conserved among different Listeria species. Other species, such as E. coli and Klebsiella pneumoniae, have similar 5’ UTR length distributions to Listeria [49]. However, some bacteria have longer or shorter median 5’ UTR lengths, such as Prochlorococcus MED4 (27 nucleotides) [50] and Synechocystis sp. PCC6803 (42 nucleotides) [51]. These variations may reflect the adaptation of different bacteria to their environmental conditions.

Prokaryotic promoter regions generally include − 35 and − 10 elements, which affect promoter activity [52]. By promoter motif analysis, we found a clear TATAAT-like − 10 element in L. monocytogenes RO15, but the − 35 element had a weak motif signal and was not well-defined. E. coli also had a weak − 35 element motif in a similar analysis [43]. We hypothesized that L. monocytogenes RO15 had many promoters without a -35 element or with a poorly conserved one. Moreover, we found extended − 10 elements, such as the “TG” motif (or -15 element) [53], in many promoter regions in L. monocytogenes RO15. These extended − 10 elements could replace the missing − 35 element and enhance transcription initiation.

In this study, we used a combination of Illumina short RNA-seq reads and ONT long direct RNA-seq reads to predict the operon structure of L. monocytogenes RO15. Long RNA-seq reads, with the potential to capture entire operons within a single read, are particularly valuable for operon prediction, as they provide direct evidence of transcriptional units. Our analysis revealed that 71% of the genes in the RO15 genome were organized into operons. This observation aligns with previous findings in other pathogenic bacteria, such as Mycobacterium tuberculosis, where 75% of genes were predicted to be operon-associated using the same computational approach [42]. In contrast, a lower percentage of operons (60%) were identified in L. monocytogenes EGD-e using a different prediction method [54]. This discrepancy likely reflects the sensitivity and specificity of different prediction tools. To our knowledge, this study represents the first application of long direct RNA-seq reads for operon prediction in L. monocytogenes. Manual inspection of the long reads supported the operon structures we predicted. For instance, the manXYZ operon previously identified in E. coli [55], were also predicted in our analysis. These observations demonstrate the validity and accuracy of our operon predictions. Thus, our findings provide a valuable reference for future studies investigating operon organization in L. monocytogenes.

Our analysis on published RNA-seq data [13] revealed that several genes in both RO15 and ScottA strains of L. monocytogenes had A-to-I RNA variants, especially after HPP. Previous studies have shown that A-to-I RNA editing can modulate the toxicity of E. coli [22] and the oxidative stress tolerance of P. putida [23] by altering the transcripts of hokB and fliC, respectively. We therefore hypothesized that A-to-I RNA editing might be a mechanism of HPP stress adaptation in L. monocytogenes. We found that genes spoVG, pbpX, mdxE, patB, hpf, and pepC were frequently edited in both strains under HPP stress, indicating that these genes might be involved in the HPP stress response. Among them, the gene hpf, which encodes a ribosome hibernation-promoting factor, was of particular interest, as the editing frequency increased over time in the treated samples. The gene hpf plays a crucial role in Listeria survival under stress conditions, by converting active 70S ribosomes into inactive 100S ribosomes [56]. We speculated that L. monocytogenes might enhance the ribosome hibernation efficiency by editing the hpf transcript during HPP recovery. Interestingly, some of the A-to-I RNA editing variants were also present in the control samples, implying that A-to-I RNA editing is not only a stress-specific phenomenon, but also a dynamic process that might occur under normal growth conditions in certain genes.

N6-methyladenosine (m6A) is a common RNA modification that modulates the structure and function of RNA molecules [57]. In bacteria, m6A is involved in various cellular processes, such as gene expression and stress response [57]. It is known that m6A responds to various environmental factors, such as temperature, oxidative stress, and antibiotic exposure, and that it influences bacterial adaptation [57]. In this study, we used a novel model [41] to detect m6A RNA modifications in direct RNA-seq reads without requiring IVT or knockout controls [41]. Our analysis revealed that HPP treatment had opposite impacts on the m6A levels of two L. monocytogenes strains: RO15 and ScottA. While RO15 showed more m6A sites after HPP treatment, ScottA showed fewer. This indicates that HPP treatment can alter the m6A landscape of L. monocytogenes RNA in a strain-dependent manner. The m6A modification may be involved in the regulation of genes related to carbohydrate transport and stress response in L. monocytogenes. For example, we found that HPP treatment increased the m6A modification of genes encoding mannose, manganese, and maltodextrin transporters in RO15, which may enhance the uptake of these nutrients and promote bacterial growth and survival. These findings indicate that m6A modification may play a role in the adaptation of L. monocytogenes to HPP treatment and other environmental stresses. However, we only used one treated and one control sample per strain in this study. More samples with statistical analysis are needed to confirm and generalize our results. Nevertheless, our study provides a useful guide for future research on the m6A modification of bacterial RNA.

Collectively, our findings shed light on the complex transcriptional landscape, stress response mechanisms, and post-transcriptional modifications in L. monocytogenes. This knowledge deepens our understanding of L. monocytogenes and opens avenues for future research exploring the functional consequences of A-to-I editing and m6A modifications in L. monocytogenes stress adaptation. This understanding can contribute to the development of improved strategies for food safety and control of Listeria. We encourage further research to validate the functional significance of these discoveries, potentially opening new avenues for understanding and combating L. monocytogenes-related challenges.

small RNA (sRNA), antisense sRNA (asRNA), high pressure processing (HPP), transcription start site (TSS), Oxford Nanopore Technology (ONT), untranslated region (UTR), open reading frame (ORF), coding DNA sequence (CDS)

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Availability of data and material

The data for this study have been deposited in the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PRJEB51821 (https://www.ebi.ac.uk/ena/browser/view/PRJEB51821). The data can be also directly downloaded using https://zenodo.org/record/6463240 (DOI: 10.5281/zenodo.6463240)

Competing interests

The authors have no competing interests to declare.

Funding

This study was supported by grants from the Academy of Finland to PA (311717, 307856, and 323576).

Authors' contributions

PA conceived and designed the study. AY performed Cappable-seq and direct RNA-seq preparations. LP organized NGS assays. ICD performed the bioinformatics analyses. LGG was involved in the preparation of the samples. PA and ICD drafted the manuscript. LGG and CUR performed a critical review of the manuscript. All authors have read, commented on, and approved the final manuscript.

Acknowledgements

We thank Eeva-Marja Turkki for performing the NGS procedures for this project. We acknowledge the CSC – IT Center for Science, Finland, for computational resources. Daniela Borda and Anca Ioana Nicolau are thanked for useful comments, and the University of Helsinki Language Services for English language revision.

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No competing interests reported.

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Cappable-Seq and Direct RNA Sequencing Reveals Novel insights into the Transcriptome of Listeria monocytogenes

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Abstract

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Background

Methods

Experimental setup, sampling, RNA extraction

Cappable-seq library processing and sequencing

Read processing and mapping for TSS

Identification of TSSs in RO15

Identification of UTR and sRNA in RO15

EGD-e TSS lift over to RO15

Variant calling

Direct RNA-seq

m6A RNA modification prediction using direct RNA-seq

Annotation of operons

Results

TSS of prophages

Discussion

Conclusions

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Availability of data and material

Competing interests

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Authors' contributions

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